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Вернуться к Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

Отзывы учащихся о курсе Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization от партнера deeplearning.ai

4.9
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Оценки: 60,803
Рецензии: 7,045

О курсе

In the second course of the Deep Learning Specialization, you will open the deep learning black box to understand the processes that drive performance and generate good results systematically. By the end, you will learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking; implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence; and implement a neural network in TensorFlow. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI....

Лучшие рецензии

HD

5 дек. 2019 г.

I enjoyed it, it is really helpful, id like to have the oportunity to implement all these deeply in a real example.\n\nthe only thing i didn't have completely clear is the barch norm, it is so confuse

NA

13 янв. 2020 г.

After completion of this course I know which values to look at if my ML model is not performing up to the task. It is a detailed but not too complicated course to understand the parameters used by ML.

Фильтр по:

376–400 из 6,996 отзывов о курсе Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

автор: Kai-Peter M

28 окт. 2019 г.

Great course!!! The best online course I have ever taken! I enjoyed almost every day I participated in that course, really an educational treasure! It is so comprehensive and detailed at the same time. Due to the good presentation of the topics it was really understandable.

автор: Mayank A

26 сент. 2020 г.

Very conceptual learning related to deep learning. Really appreciate the mentor who is teaching from the depth and able to clear all the concepts related to course topics. Highly recommend to those who haven't gone through all the videos and programming assignments.Thanks!

автор: Saif M P

8 мая 2020 г.

I have really learned a lot of things! It surely took 3 weeks to complete all the things, it was tough at some points, but if I didn't do this course, I might have some regrets that I didn't achieve all the knowledge. Thanks to Mr. Andrew, he is really a very good teacher.

автор: Mandar K

21 июля 2019 г.

Wonderful course and material. Andrew has a great way of explaining the topics in the simplest way. Although I had some issue with understanding the optimizers, I learned a great deal. However, This course needs a revamp using Tensorflow 2.0 for the tutorials :) Thank you

автор: Amit G

6 нояб. 2017 г.

there is a lot of materiel that is being discussed during the lectures, and all of it seems like it could be really relevant. I am missing a consolidated course deck - ie something like a deck of slides on all the important concepts that are being discussed, for reference.

автор: Kiet L

26 авг. 2017 г.

Another awesome course by Andrew. I wish he was my professor in my grad school. I hope Coursera publishes all the notebooks + data on public github so I can redo all the exercise again. Too much info to digest in short amount of time. I can't wait for RNN and CNN courses.

автор: Dan N M

5 янв. 2021 г.

Excellent course. A great way to understand the fundamentals. It's always good to understand what's under the hood as frameworks abstract away a lot of the hard work going on underneath. Also makes you aware of how to be better tune and understand hyperparameters etc.

автор: Syed M H J

8 янв. 2019 г.

Easily the best course on diving under the hood of how a Neural Network actually works and how to tune to the satisfaction of our results.

A no brainer for sure. The best part the exercises. You MUST do the exercises to understand thoroughly how the systems actually work.

автор: WAN L

1 авг. 2018 г.

I like this course, it details basic while popular technique we need to optimize neural networks. also the lectures on different optimization algorithms are very helpful for you to know details on how they run when we choose these algorithm in frameworks like tensorflow.

автор: Akash M

13 июля 2020 г.

I found this course extremely helpful. It enabled me to develop a really good intuition about how deep learning models are made, and what are the small steps that go a long way in improving the overall performance of the system. I hope all of you find this helpful too.

автор: Arun G

22 мар. 2020 г.

Excellent course, giving a very good insight into how to approach building a deep neural network, the concepts of various parameters, tips on how to best achieve a good algorithm and a step by step walk through of the different algorithms, parameters and optimization.

автор: Yash M B

22 окт. 2019 г.

Quite detailed curriculum. It is a great continuation for course 1 of this specialization series. As usual, Prof. Andrew Ng is there to guide our way throughout the course duration. A really fun and intriguing course which can lead to course 3 as a proper continuation.

автор: PeterStephenson

26 июня 2019 г.

This course was perfect for me. I thought it was a good balance between theory and practice. I don't think I'm ready to start building NN's from scratch, but at least now I know how to get started. Also, I now have an understanding of the complexity of a ML project.

автор: SHUBHAM G

18 июня 2018 г.

Mini Batch/Adam Optimization concepts was very well explained. I was expecting the detailed derivation of the backpropagation for the batch normalization case. Overall it was a great course and it greatly improved my understanding about concepts used in deep learning.

автор: Favio A C

3 нояб. 2017 г.

4.5/5 A diferencia del primer curso que es una continuacion del de Machine Learning de Andrew Ng , aqui vemos una evolución del contenido , se pasa a ver miniBatch Gradient Descent, Regularizacion , Momentum , Adam , y un inicio a tensorflow

realmente un MUY BUEN Curso

автор: Huaishan Z

1 окт. 2017 г.

Through the class, the tuning of Hyperparameter is detailed introduced and more important is that why it's tuned is very clear. Suggest persons study deep learning to study this class carefully.

Expect to have more info from the current study in University or College.

автор: John R

24 июля 2019 г.

I guess the difficulty is what you make of it, with further studying and dedication, but I would like to encounter more challenging assignments, where one has to code entire cells for instance, as opposed to a single line here and there.

But everything else is great!

автор: Janzaib M

4 мар. 2018 г.

Contains very good understanding of Hyperparameters and their tuning process.

Secondly, teaches very well the mathematics of optimizers such as GD, SGD, GD with Momentum, GD with RMSProp and ADAM.

Finally, a small glimpse of Batch Normalization.

Highly Recommended!!!!!!

автор: Frank I

25 авг. 2017 г.

I had previously used optimizers with momentum and variance momentum (Adam) with the understanding that they helped without knowing exactly how. This course cleared up all those tiny details and has left with with a greater appreciation of neural networks in general.

автор: Quentin M

1 авг. 2021 г.

Although this particular course is not as sexy in its applications as the others it is still vital information for any serious practictioner. Prog Ng shares his years of experience and you really feel that with each video you are learning invaluable tips and tricks.

автор: Nguyễn V A

29 мая 2021 г.

This is an amazing course. I've learned a lot from this course, really amazing on how to tune these hyperparameters. I think this course would be a great course that you should have if you want to become an AI engineer! Thank Coursera a lot! Everything is amazing!!!

автор: Igor A G d O

12 сент. 2020 г.

This was a great course. I could develop solid intuition about how neural networks work, and learn about state-of-the-art ways to make them better. The only thing that I have to complain about is the fact that the Tensorflow part should be updated to Tensorflow 2.0.

автор: Thomas N

9 окт. 2019 г.

This course broadened my understanding of what really happens when driving the cost function closer to its minimum and techniques to go there faster. I found this course instructive and the programming excercises helped a lot to digest the learnings from the videos.

автор: Saikiran K

3 авг. 2018 г.

I know deep learning already, but I saw many people who even know it doing this specialization,so i too started like that..but its a very good experience concepts are very well explaining and I am enjoying assignments a lot it a very fun experience doing all again..

автор: Narek A

8 окт. 2017 г.

I find this course very useful, many complex ideas are presented in a very understandable way! This course is like a collection of all important aspects! However, homework could be more difficult, because now almost all the answers are given in the python notebooks.